Human activity monitoring device

Abstract

A method for monitoring human activity using an inertial sensor includes continuously determining an orientation of the inertial sensor, assigning a dominant axis, updating the dominant axis as the orientation of the inertial sensor changes, and counting periodic human motions by monitoring accelerations relative to the dominant axis.

1. A method comprising:
 detecting motion by an inertial sensor included in a mobile device; 
 determining, by the mobile device, whether the motion has a motion signature indicative of a user activity that the mobile device is configured to monitor; 
 when the motion does not have a motion signature of a user activity that the mobile device is configured to monitor, entering a sleep mode. 
 
     
     
       2. The method of  claim 1 , further comprising:
 when the motion does have a motion signature of a user activity that the mobile device is configured to monitor, monitoring for future motions having the motion signature. 
 
     
     
       3. The method of  claim 1 , further comprising, while the mobile device is in the sleep mode:
 periodically sampling acceleration data at a predetermined sampling rate, wherein each sample includes acceleration data measured by the inertial sensor over a predetermined time period; and 
 when acceleration data having a motion signature indicative of a user activity that the mobile device configured to monitor is detected within the predetermined time period, exiting the sleep mode. 
 
     
     
       4. The method of  claim 1 , wherein the inertial sensor has an inertial wakeup functionality, the method further comprising, while the mobile device is in the sleep mode:
 detecting a motion sufficient to trigger the inertial wakeup; 
 sampling acceleration data for a predetermined time period; 
 determining whether the acceleration data includes a motion signature indicative of a user activity that the mobile device is configured to monitor; and 
 when the acceleration data includes a motion signature indicative of a user activity that the mobile device is configured to monitor, exiting the sleep mode. 
 
     
     
       5. A method for a mobile device comprising:
 receiving acceleration data that meets stepping criteria from an accelerometer included in the mobile device; 
 incrementing a step count in a step count buffer; 
 when at least one of a) the step count is below a step count threshold, or b) a current user cadence fails to match a step cadence of a user profile, using a default step cadence window to identify a time frame within which to monitor for a next step; and 
 when the step count is at or above the step count threshold, determining a dynamic step cadence window and using the dynamic step cadence window to identify the time frame within which to monitor for the next step. 
 
     
     
       6. The method of  claim 5 , wherein the step count buffer represents probable steps, the method further comprising:
 emptying the step count buffer and acknowledging the step counts from the step count buffer as actual steps when the step count buffer reaches the step count threshold; and 
 entering a stepping mode upon emptying the step count buffer. 
 
     
     
       7. The method of  claim 5 , further comprising, upon determining the dynamic step cadence window:
 examining previous acceleration data to determine whether any additional steps would have been counted if the dynamic step cadence window had been used when the previous acceleration data was received; and 
 counting those additional steps. 
 
     
     
       8. The method of  claim 5 , wherein determining the dynamic step cadence window comprises:
 computing a rolling average of stepping periods of previously counted steps; and 
 setting the dynamic step cadence window based on the rolling average of stepping periods. 
 
     
     
       9. The method of  claim 5 , wherein the stepping criteria comprise:
 a first criterion that is satisfied when a current acceleration measurement has a greater magnitude than a previous acceleration measurement; 
 a second criterion that is satisfied when the current acceleration measurement has a greater magnitude than a lower threshold; and 
 a third criterion that is satisfied when the current acceleration measurement has a lesser magnitude than an upper threshold. 
 
     
     
       10. The method of  claim 5 , further comprising:
 determining an orientation of the mobile device with respect to gravity; 
 assigning a dominant axis based on the orientation; and 
 comparing only acceleration data for the dominant axis to the to the stepping criteria to make a determination that the acceleration data meets the stepping criteria. 
 
     
     
       11. The method of  claim 5 , further comprising:
 when the current user cadence matches the step cadence of a user profile, using a stored step cadence window of the user profile to identify the time frame within which to monitor for the next step.